Controlling code growth by dynamically shaping the genotype size distribution
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Gardner:2015:GPEM,
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author = "Marc-Andre Gardner and Christian Gagne and
Marc Parizeau",
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title = "Controlling code growth by dynamically shaping the
genotype size distribution",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2015",
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volume = "16",
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number = "4",
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pages = "455--498",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Bloat
control, Monte Carlo methods",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-015-9242-8",
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size = "44 pages",
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abstract = "Genetic programming is a hyperheuristic optimisation
approach that seeks to evolve various forms of symbolic
computer programs, in order to solve a wide range of
problems. However, the approach can be severely
hindered by a significant computational burden and
stagnation of the evolution caused by uncontrolled code
growth. This paper introduces HARM-GP, a novel operator
equalisation method that conducts an adaptive shaping
of the genotype size distribution of individuals in
order to effectively control code growth. Its
probabilistic nature minimises the computational
overheads on the evolutionary process while its generic
formulation allows it to remain independent of both the
problem and the genetic variation operators.
Comparative results over twelve problems with different
dynamics, and over nine other algorithms taken from the
literature, show that HARM-GP is excellent at
controlling code growth while maintaining good overall
performance. Results also demonstrate the effectiveness
of HARM-GP at limiting overfitting in real-world
supervised learning problems.",
- }
Genetic Programming entries for
Marc-Andre Gardner
Christian Gagne
Marc Parizeau
Citations